Structural Equation and OLS Models with Self-Conception Measures

Tables 3.3 through 3.5 present the structural equation and OLS regression models predicting sex segregation scores of college degrees and the change in sex segregation scores of students‘ majors between year 1 and graduation. The models in Tables 3.3 include the feminine self-conception latent measure plus controls for school,

race/ethnicity, and SAT math and verbal scores. I present the models separately for men and women. In the first set of columns in 3.3, the feminine self-conceptions latent measure is significant and positive for women. This means that every point more feminine women perceive themselves to be corresponds to 6.9% more women in their college degree fields. Thus, women who perceive themselves as more feminine than their peers are more likely to graduate from a female-dominated major, net of school, race/ethnicity, and SAT scores, and those who perceive themselves as more masculine than their peers are more likely to graduate from a male-dominated major.

The second set of models in Table 3.3 predicts the change in sex segregation scores between college entry and graduation. To the first set of models in this table, I add the sex segregation score of students‘ year 1 majors. This addition effectively means that the dependent variable in the second set of models indexes the change in sex segregation scores between college entry and graduation. Masculine or feminine self-conceptions are not a significant predictor of changing into more female- or male-dominated majors in college for either men or women.

A similar trend holds for unsystematic self-conceptions (Table 3.4). Women who perceive themselves as less systematic then their women peers are more likely to

graduate with a female-dominated degree. Men who perceive themselves as less

systematic then their peers are also more likely to earn a female-dominated degree. It is important that this result holds net of students‘ performance on math and verbal tasks:

among students with the same math SAT score, students who perceive themselves as less systematic than their peers will be more likely to choose female-dominated degrees.

Controlling for SAT scores helps illustrate that unsystematic self-conceptions are not tapping into students‘ actual abilities; but rather their perception of their logical skills—

perceptions that have been shown by social-psychological literature to be fairly

inaccurate at capturing people‘s objective skills, and, more importantly, such perceptions are highly gendered (Correll 2004, 2001). In this process of self-expression, students appear to be matching their self-perceptions (which may or may not be related to their actual skills) to stereotypes about the fields in which they choose their degrees.

Unsystematic self-conceptions are not a significant predictor of changing majors, however.

Table 3.5 includes the people-oriented self-conception measure. Here again, women who perceive themselves as more people-oriented then their peers are more likely to earn female-dominated degrees, and women who perceive themselves as more things-oriented are more likely to earn male-dominate degrees. People-things-oriented

self-conceptions are not a significant predictor of the sex segregation score of men‘s degree choices. However, men who have people-oriented self-conceptions are more likely to change into a more female-dominated major over the course of their college careers.

A few interesting racial/ethnic differences arose once I controlled for gendered self-conceptions. Net of unsystematic self-conceptions (Tables 3.4), African-American men are more likely to enter a male-dominated degree than similar white men. African-American men are also more likely to earn an engineering degree than white men (Table 3.11). Among women, net of gendered self-conceptions, African-American women are more likely than white women to earn a physical sciences degree (Table 3.10) but

marginally less likely to earn a biology degree (Table 3.9). These results could be due to the combined result of selection effects of high-achieving African-American students into the schools in my study (Gerber & Cheung 2008) and, perhaps, the existence of support programs for under-represented minority students in male-dominated majors such as science and engineering and a lack of them for female-dominated majors. I also find that, net of unsystematic and people-oriented self-conceptions, Asian and

American men are more likely than white men to earn female-dominated degrees. Asian-American men face countervailing stereotypes as both less hegemonically masculine than white men (Connell 2005), but also as ―naturally‖ talented at math and science-related fields, compared to white men (Eglash 2003). Perhaps the former stereotype is helping to produce this result. I hope that this analysis will be conducted across other U.S.

institutions to help determine whether these racial/ethnic differences are the result of a selection process or a trend across higher education in the U.S.

Discussion

These results illustrate that the students in this sample are reproducing occupational sex segregation in their college major choices. Men choose more male-dominated majors and degrees than women and women choose more female-male-dominated

majors and degrees than men. While I found that many men and women changed their majors over the course of their time in college, this switching neither aggravates nor undermines the segregation trends in their initial choice of college majors. Thus, is seems that the early choices students make about their college majors are the most important in reproducing sex segregation throughout college.

I also found that self-conceptions predict whether men and women graduate with male- or female-dominated degrees: women who perceive themselves as feminine or unsystematic and men who perceive themselves as less systematic compared to their peers are more likely to earn a female-dominated degree. Women with people-oriented self-conceptions are more likely to earn a female-dominated degree than women with more things-oriented self-conceptions, and men with people-oriented self-conceptions are more likely switch into more female-dominated majors while in college. Thus, it is not just that women and men help to reproduce the demographic imbalance of their fields of study, but rather those who are most likely to choose the most sex-segregated majors are also those who are most likely to hold the most stereotypical perceptions of themselves (women as feminine, unsystematic and people-oriented; men as systematic and things-oriented).

The sex segregation scores help illustrate respondents‘ individual contributions to reproducing sex segregation, and are a single metric for examining the effect of self-conceptions on where men and women place themselves along this spectrum in response to stereotypes about male- and female-dominated fields. However, it is also useful to understand how self-conceptions predict whether men and women enter individual fields along that spectrum of female-dominated to male-dominated fields. The next section

examines the relationship between men‘s and women‘s self-conceptions and their likelihood of choosing two culturally female-typed majors (social sciences and humanities), two relatively gender-neutral majors (business and biology), and two culturally male-typed majors (engineering and physical sciences). Looking at these individual fields will help understand whether this is a spectrum-wide phenomenon, largely driven by general stereotypes about male-dominated and female-dominated, or whether the results just presented are largely driven by students‘ self-expressive responses to the stereotypes of a few specific male-dominated or female-dominated fields.